Dr. Mario's 2nd 🧠

Metric Trees for Digital Analysts

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When someone suggests a revenue-related North Star metric, I struggle with it. Sure, if you’re running a for-profit business, some of your lagging or output metrics will involve revenue. But saying “profit margins are our North Star metric” is like stating the obvious - we need air to breathe. (View Highlight)

I started to rediscover my appreciation for metrics when I came across Abhi’s work with metric trees. This wasn’t actually a new concept - I’d learned about it in university under a different name: the DuPont schema. (View Highlight)

In my design process, I often create an extensive model first. But the next step is usually simplifying it because you have to work with it practically. A tree with 300 metrics means you need systems to calculate, visualize, and act on all 300 metrics. While possible, this requires a sophisticated organizational setup. That’s why I recommend simplifying when possible. (View Highlight)

Metric trees can also include dimensions, which work like filters. While I wouldn’t recommend this for beginners, it’s possible to include dimensions like campaign sources to filter your tree based on where users come from. This is straightforward in design but can be challenging in practice - some metrics might have certain dimensions while others don’t, leading to attribution problems that are often difficult to solve. Until you can represent your tree with actual numbers, I’d suggest leaving out these design options. (View Highlight)

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